Determining drug effectiveness ranking for a patient using machine learning

ABSTRACT

Computer based methods, systems, and computer readable media for intelligently accessing various types of pharmaceutical information in a content repository and ranking drugs at the variant level, gene level, and pathway level. In some cases, drugs that target the same gene, gene variant, or biological pathway may be ranked based upon in vitro, pre-clinical, clinical, or post-clinical evidence. To determine ranking of a plurality of drugs, information pertaining to drug administration is analyzed for the drugs. For a plurality of drugs, attributes corresponding to the drug are determined, wherein the attributes include a variant or a gene targeted by the drug, and a biological pathway comprising the targeted variant or gene. The plurality of drugs are ranked according to a drug effectiveness score based on one or more of a determined efficacy, potency, or toxicity.

1. TECHNICAL FIELD

Present invention embodiments relate to drug effectiveness, and morespecifically, to using machine learning to analyze drug information togenerate drug effectiveness rankings for a patient and to intelligentlysearch and extract content related to drug administration.

2. DISCUSSION OF THE RELATED ART

Databases and article repositories often contain a large corpus ofdocuments of varying types of information. For example, a user maysearch NCBI's PubMed® database for different types of peer-reviewedscientific and clinical documents. Numerous drugs may potentially beavailable to treat patients who have diseases driven by a common genomicalteration such as HER-2 positive breast cancers. However, the efficacyand potency of each of these individual drugs often varies significantlyamong patients.

While there are a variety of databases available which cover clinicaland experimental information, these databases do not adequately coverspecialized information pertaining to pharmaceutical drugs andbiologics. Although some systems are geared towards anticancertreatments, covering approved and investigational drugs, these systemsdo not provide specific and specialized information regarding drugefficacy, potency, and other aspects related to drug administration.

Additionally, access to full-length research documents in PubMed® isoften granted only if an institutional license agreement has beenimplemented with the journal's publisher or another form of payment hasbeen submitted to acquire the rights to the full-length document. Toensure the accuracy of the data, users must be able to evaluate figures,graphs, tables and text within the results section of the documents. Insome cases, content repositories may maintain millions of documents withno intelligent way to access complete content.

Content repositories do not provide user interfaces for specific contentsearching pertaining to efficacy and other features of pharmaceuticaldrugs and biologics. Accordingly, relevant information is often missed,and patients may not be matched with optimal drugs or combinationsthereof.

SUMMARY

According to embodiments of the present invention, methods, systems andcomputer readable media are provided for intelligently accessing varioustypes of pharmaceutical information in a content repository and rankingdrugs at the gene variant level, gene level, and biological pathwaylevel. In some cases, drugs that target the same gene/biological pathwaymay be ranked based upon pre-clinical, clinical, or post-clinicalevidence including drug characteristics.

Extracted information pertaining to drug characteristics is analyzed fora plurality of drugs. For each drug, one or more drug characteristicsincluding toxicity, potency, and/or efficacy are determined. The drug isassociated with a plurality of attributes including a variant or a genetargeted by the drug, and a biological pathway comprising the targetedvariant or gene. The plurality of drugs are ranked according to a drugeffectiveness score based on the drug characteristics. This approachrelies on clinical evidence to ascertain drug properties in order toprovide an optimal or effective ranking of drugs for a specified target.

In some aspects, drug characteristics may include any parameter used toevaluate drug performance or effectiveness including efficacy, toxicity,and potency. Present techniques allow for drugs with high potency andefficacy and low toxicity to be identified and prioritized foradministration to a patient. Drug characteristics are not limited topotency, efficacy and toxicity, as any suitable drug characteristic maybe used to identify and rank drugs.

In another embodiment, the extracted information comprises pre-clinical,clinical, and post clinical information, from which drug characteristicsare extracted. Thus, present techniques utilize evidence from theclinical literature. This information may be used to create a trainingdata set for a machine learning module to predict drug characteristicsof other drugs for which such characteristics may not be available inthe literature.

In other aspects, a machine learning module may be trained with trainingdata sets generated from the extracted information. For drugs not in thetraining data set, one or more drug characteristics for each drug of theplurality of drugs may be predicted by the trained machine learningmodule. The drugs may be ranked for treatment of a patient-specificcancer, wherein each drug targets a particular gene, gene variant, orbiological pathway associated with the patient's cancer, and wherein theranking is based on the predicted drug characteristics according to adrug effectiveness score. Accordingly, present techniques provideranking information on drugs for a specific patient.

In other aspects, for a plurality of drugs, common structural featuresand corresponding drug characteristics may be identified, includingtoxicity. Using a machine learning module trained on informationidentifying the common structural features and the drug characteristics,other drugs may be predicted to be associated or not associated withtoxicity. Likewise, drugs with a high risk of toxicity may be analyzedby a machine learning module to predict chemical structures associatedwith toxicity. Present techniques predict toxicity, and this result maybe used to identify optimal drugs having a low risk of toxicity andother undesirable side effects.

In other aspects, the plurality of drugs may be classified into groupsbased on a common target, and the drugs may be ranked within each group.Thus, optimal drugs for a specific target (and for a specific patient)may be identified in an effective manner.

In other aspects, drug attributes comprise patient-specific informationindicating a gene, a gene variant or a biological pathway for a patient,and further comprise identifying a plurality of drugs that target thegene, the gene variant or the biological pathway(s) of the patient. Theidentified drugs may be ranked according to a drug effectiveness scorebased on the drug characteristics. Thus, drugs may be ranked based onattributes and drug characteristics according to a drug effectivenessscore in a patient-specific manner.

It is to be understood that the Summary is not intended to identify keyor essential features of embodiments of the present disclosure, nor isit intended to be used to limit the scope of the present disclosure.Other features of the present disclosure will become easilycomprehensible through the description below.

BRIEF DESCRIPTION OF THE DRAWINGS

Generally, like reference numerals in the various figures are utilizedto designate like components.

FIG. 1 is a block diagram of an example computing environment for thedrug ranking system, according to embodiments of the present disclosure.

FIG. 2 is an example flow chart of a manner of ranking drugs based on adrug effectiveness score, according to embodiments of the presentdisclosure.

FIG. 3 is an illustration showing an example of different types of omicdata, according to embodiments of the present disclosure.

FIG. 4 is an illustration showing an example of different biologicalpathways specific to cancer, according to embodiments of the presentdisclosure.

FIG. 5 is an illustration showing an example of different drug profiles,according to embodiments of the present disclosure.

FIG. 6 is an illustration showing an example flowchart of ranking drugsbased on drug characteristics, according to embodiments of the presentdisclosure.

FIG. 7 is a high level flow diagram showing a manner of ranking drugs,according to embodiments of the present disclosure.

DETAILED DESCRIPTION

Personalized cancer medicine involves matching an oncogenic mutationfrom the patient to the appropriate targeted drug therapy. A drug (e.g.,pharmaceutical, biologic, etc.) may include any suitable therapy inpre-clinical, clinical, or post-clinical studies. Methods, systems, andcomputer readable media are provided herein to rank drugs according to adrug effectiveness score based on drug characteristics for a specificpatient.

Documents pertaining to pre-clinical, clinical, or approved drugs may beaccessed and analyzed to determine relationships between drugs, genes,gene variants, and biological pathways for a specific disease such as atype of cancer. A user interface, which may be provided within adocument management portal, enables the user to query a databaseregarding drug efficacy or other drug characteristics (e.g., drugpotency, drug toxicity, etc.) for a particular type of cancer.

To achieve this, evidence related to drug administration (e.g.,efficacy, potency, toxicity, secondary effects (such as off targeteffects), IC50, ED50, etc.) may be extracted from the clinicalinformation. Once the relevant information is extracted, drugs and theircorresponding characteristics may be analyzed by a machine learningsystem in terms of efficacy and other characteristics (e.g., toxicity,potency, etc.) at the gene level, gene variant level, or biologicalpathway level to determine optimal drugs for a specific oncogenicmutation. The drugs may be ranked for a specific patient for a specifictype of disease, such as cancer.

An example environment 100 for use with present invention embodiments isillustrated in FIG. 1 . Specifically, the environment includes one ormore server systems 10, and one or more client or end-user systems 20.Server systems 10 and client systems 20 may be remote from each otherand communicate over a network 35. The network may be implemented by anynumber of any suitable communications media (e.g., wide area network(WAN), local area network (LAN), Internet, Intranet, etc.).Alternatively, server systems 10 and client systems 20 may be local toeach other, and communicate via any appropriate local communicationmedium (e.g., local area network (LAN), hardwire, wireless link,Intranet, etc.).

Client systems 20 enable users to access documents and information(e.g., clinical documents, extracted literature data, extracted omicsdata, drug ranking information, patient-specific information, biologicalpathways, etc.) from server systems 10 for analysis and review. Theserver system may include a drug ranking system 15 to rank drugs inorder to select and prioritize drugs for a specific patient.

A database system 40 may store various information for the analysis(e.g., extracted omics data 41, extracted literature data 42, patientspecific data 43, drug ranking data 44, etc.). The database system maybe implemented by any conventional or other database or storage unit,may be local to or remote from server systems 10 and client systems 20,and may communicate via any appropriate communication medium (e.g.,local area network (LAN), wide area network (WAN), Internet, hardwire,wireless link, Intranet, etc.). The client systems may present agraphical user (e.g., GUI, etc.) or other interface (e.g., command lineprompts, menu screens, etc.) to solicit information from userspertaining to the desired documents and drug analysis, and may providereports including analysis results (e.g., drug efficacy, ranking ofdrugs, toxicity of drugs, potency of drugs, ED50, IC50, biologicaltargets of drugs (e.g., genes, gene variants, proteins, other cellulartargets), biological pathways, secondary effects such as off targeteffects, etc.).

Server systems 10 and client systems 20 may be implemented by anyconventional or other computer systems preferably equipped with adisplay or monitor 26, a base (e.g., including at least one processor16, 22 one or more memories 17, 23 and/or internal or external networkinterfaces or communications devices 18, 24 (e.g., modem, network cards,etc.)), optional input devices (e.g., a keyboard, mouse or other inputdevice) and/or user interface 19, 25 and any commercially available andcustom software (e.g., server/communications software, drug rankingsystem 15, browser/interface software, etc.).

Alternatively, one or more client systems 20 may analyze documents todetermine drug ranking when operating as a stand-alone unit. In astand-alone mode of operation, the client system stores or has access toextracted omics data 41, extracted literature data 42, patient-specificdata 43, and drug ranking data 44 as well as the drug ranking system 15.The graphical user (e.g., GUI, etc.) or other user interface (e.g.,command line prompts, menu screens, etc.) may solicit information from acorresponding user pertaining to the drug ranking, and may providereports including analysis results and drug ranking (e.g., ranking ofdrugs, drug efficacy, drug toxicity, drug potency, IC50, ED50,biological targets of drugs (genes, gene variants, proteins, othercellular targets), biological pathways, secondary effects such as offtarget effects, etc.).

Extracted omics data 41 and extracted literature data 42 may includeextracted information from databases and/or literature that may indicatethe presence of a disease in a patient. For example, extractedliterature data may include genes and gene variants associated withdiseases, along with corresponding expressed proteins, transcripts, orother relevant molecules, biological pathways, drug targets, toxicities,potencies, efficacies, secondary effects, IC50, ED50, etc. Literaturedocuments are presumed to be readable by a machine reader. In someaspects, optical character recognition may be used to recognize text ina document, to render the text readable and searchable. Additionally,text in tables, images, image captions, or lists may also be renderedmachine readable. This processing ensures that images of documents,e.g., scanned PDFs, are included in the analysis.

Literature data may include data from databases, scientific literature,and clinical and preclinical literature, as well as any other source ofrelevant information, which relates to biological targets of specificdrugs that are in clinical trials or that have been approved byregulatory agencies. In some cases, this information includes a druginteraction with a specific biological molecule of a pathway (e.g.,evidence that a drug binds to a biological molecule, inhibits abiological pathway, activates a biological pathway, off target effectsincluding interaction with a secondary target, adverse effects,contraindications with other medicines, etc.). This allows biologicaltargets to be associated with biological pathways, and a framework to beset up to study drug efficacy and specificity.

In some aspects, diseases may include a type of cancer such as breast,lung, pancreatic, ovarian, prostate, etc. In some aspects, relevantterms to be extracted by the drug terms extractor may be provided (e.g.,by a subject matter expert) wherein the search terms comprise genes,gene synonyms, gene variants, gene variant synonyms, drugs, drugsynonyms, diseases, disease synonyms or cancer-types and cancer-typename synonyms.

Extracted omics data 41 may include information regarding genes/genevariants associated with diseases, RNA translation levels associatedwith diseases, protein expression levels associated with diseases, etc.for a population of patients. In some cases, the data may be grouped toform a cohort based upon common features of the population of patients.

Patient-specific data 43 may include omic data specific to the patient(not data from a population of patients) along with other medicalhistory data (e.g., drug allergies, age, medical conditions, othermedications to assess for contraindications, etc.) for the specificpatient.

Drug ranking data 44 may include rankings of a list of drugs for aspecific disease, such as a type of cancer. In some cases, the rankingsmay reflect a population of patients, rather than specific informationfor a particular patient. In other cases, the rankings may be refined tobe specific to a particular patient, considering patient-specificoncogenic mutations, drug allergies, contraindications from othermedications, etc. that may apply to that patient.

Drug ranking system 15 may include one or more modules or units toperform the various functions of present invention embodiments describedherein. The various modules (e.g., drug terms extractor 71, omicsextractor 72, machine learning module 73, biological pathways module 74,drug ranking module 75, etc.) may be implemented by any combination ofany quantity of software and/or hardware modules or units, and mayreside within memory 17, 23 of the server and/or client systems forexecution by processor 16, 22.

Drug terms extractor 71 parses literature in machine readable form(e.g., such as scientific or clinical publications comprisinginformation including clinical information, etc. and/or databases toidentify information relating to a specific drug for a particulartherapeutic target of a biological pathway). In some cases, the drugterms extractor 71 may comprise natural language processing (NLP) module76, which may be configured to identify gene/gene variant names, proteinnames, drug names, biological targets, characteristics of drugs (e.g.,efficacies, potencies, toxicities, secondary effects, IC50, ED50, etc.)and synonyms thereof. NLP-based tools may semi-autonomously extractevidence related to drug characteristics based on gene alteration, genevariant alteration, and gene pathway alteration. These drugs may beranked in terms of their characteristics at the gene level, gene variantlevel, and/or biological pathway level.

In some aspects, drug terms extractor 71 relies on data frompre-clinical, clinical, and post-clinical studies (instead of just invitro studies), limiting drugs to those that are approved by aregulatory agency or otherwise available from a clinical trial. Often,the mechanism of the drug is known.

Additionally, gene name synonyms, gene variant name synonyms, drug namesynonyms and cancer-type name synonyms may be identified by drug termsextractor 71 and linked to the common name, in order to be included inthis analysis.

In some cases, the system may be provided with a list of drug names (andsynonyms) that are approved by the FDA or in clinical trials. Forexample, the system may be provided with the tradename, generic name,structural name, and/or reference ID (e.g., from a database of drugs)pertaining to the drug, etc. in order to identify and extract relevantinformation from the literature. In some aspects, drug terms extractor71 may extract any suitable information to determine characteristics ofa cancer drug including but not limited to efficacy, toxicity, potency,etc., terms pertaining to success of the clinical trial, termspertaining to failure of the clinical trial, number of clinical trials,phase of clinical trial, drug side effects, drug interactions, drugstructures, etc. In some cases, terms pertaining to the biologicaltarget (e.g., protein, cell surface target, cell target, intracellulartarget, extracellular target, etc.) may also be extracted by the drugterms extractor 71, while in other cases, information pertaining to thebiological target may be provided by subject matter experts.

Clinical documents (e.g., including pre-clinical, clinical andpost-clinical documents and databases) may be identified and theextraction of relevant information automated. In some cases, theextracted information may be curated by subject matter experts (e.g.,for particular types of cancer). Any suitable source may be usedincluding experimental/research articles, drug discovery articles,pre-clinical articles, clinical articles, post-clinical articles, etc.

For each drug, a variety of different types of drug related informationmay be extracted, including but not limited to drug name (includinggeneric names and synonyms), gene/protein or other biological targets ofdrug (primary target), toxicity of drug, off-target effects (secondarytargets that the drug binds to), structure of drug, potency, ED50, IC50,adverse events, patient specific information, drug efficacy, etc. Offtarget effects may refer to a drug that binds to a secondary target withlower affinity as compared to the primary target and may cause abiological effect that may adversely impact a toxicity profile of thedrug.

In some cases, the extracted information may be organized according tocancer type for analysis, according to age ranges, according to gender,according to biological pathway, according to gene/gene variant, or anyother category suitable for generating a cohort of data as compared tothe specific patient. Extracted information may be stored as structuredtext or unstructured text or as a combination thereof.

Omics extractor 72 may access omic data from various databases (e.g.,public, private, etc.), which contain data from genomics, epigenomics,transcriptomics, proteomics, metabolomics, etc. studies. The omicsextractor 72 may contain one or more extractors tailored to extract eachtype of biological data. For example, a genomic/epigenomic extractor mayextract and analyze genomic/epigenomic data including genes, genevariants, as well as genetic alterations and mutations associated withcancer. A transcriptomic extractor may extract and analyze RNAexpression profiles in cancerous biological samples (e.g., to analyzeRNA profiles showing overexpression, underexpression, or similarexpression to noncancerous controls). A proteomic extractor may extractand analyze protein expression profiles in cancerous biological samples(e.g., proteins that are overexpressed, under expressed or are about thesame as compared to noncancerous controls). Similarly, a metabolomicextractor may extract and analyze metabolic data in cancerous biologicalsamples. Biological data may include any suitable format, includingsequencing data, hybridization microarrays, transcription microarrays,expression microarrays, metabolic microarrays, etc.

Machine learning module 73 may be trained on extracted data to identifynew relationships between drugs and biological targets, to identifycauses of toxicity, such as off target effects including interactionswith a gene/gene variant protein/protein variant linked to a toxicityeffect, etc. Machine learning module 73 may be provided with trainingdata comprising information about known drugs, including structure,toxicity, biological targets, potency, efficacy, and off target effects,etc. Machine learning module may predict any of these features (e.g.,toxicity, biological targets, potency, efficacy, and off targets, etc.)for drugs to be analyzed.

Machine learning module 73 may use any suitable machine learningtechnique, including but not limited to statistical classification,supervised learning, unsupervised learning, artificial neural networks,deep learning neural networks, cluster analysis, random forest,dimensionality reduction, binary classification, decision tree, etc. topredict various features, including but not limited to toxicity,biological targets, potency, efficacy, and off targets for drugs.

Biological pathways module 74 maps information from drug terms extractor71 and/or omics extractor 72 to biological pathways. For example, a drugmay be known to interact with a first target (primary target), whereinthe target may be a gene, gene variant, transcript, protein, metabolite,etc. associated with an omic data set. The biological pathway module 74may map the first target to a first biological pathway.

In some cases, biological pathways module 74 may map secondary effects(off target) to biological pathways. This may be repeated for multipledrugs, allowing secondary effects from multiple drugs to be mapped toone or more biological pathways. Biological pathways may be determinedbased on predetermined groups of genes. In some cases, biologicalpathways may be associated with toxicity. Drugs that interact with thesepathways, through primary or secondary effects may be ranked lower thandrugs that do not interact with these pathways. Thus, not only maybiological pathways determine a drug that is suitable for a specificmutation in a patient, but the biological pathways may also be used toprioritize drugs that do not interact with toxicity associated pathways.

Drug ranking module 75 may accept inputs from the biological pathwaysmodule 74, the drug terms extractor 71, machine learning module 73,and/or the omics extractor 72 as well as patient specific data 43. A setof drugs suitable for the specific patient may be provided to the drugranking module 75, and the module may rank the drug based on positivefactors of effectiveness that result in a higher ranking (e.g., goodefficacy, high potency (e.g., nM or pM range), no known secondarytargets, low toxicity, etc.), or negative factors of effectiveness thatresult in a lower ranking (e.g., limited efficacy, low potency, multiplesecondary targets, high toxicity, etc.). Drugs targeting relevantbiological pathways may be identified based upon patient-specific data,and the extracted information regarding characteristics of drugs may beused to rank drugs for a specific patient based on omics and other data(e.g., tumor type, tumor mutation, clinical data, medical history,etc.). Based on the received information, the drug ranking module ranksthe set of drugs for a specific patient, which may be stored in drugranking data 44.

Present techniques offer high granularity regrading drug interactions,efficacies, and other characteristics and may be tailored to identifyoptimal treatments for specific patients.

FIG. 2 is a flow chart of example operations for determining drugrankings. At operation 210, drug information is extracted from clinicalliterature. Drug information may include but is not limited to primarytargets, efficacy, toxicity, side effects, potency, ED50, IC50, etc. Atoperation 220, drugs are optionally grouped by cancer type, genemutation and/or possibly other patient-specific factors. At operation230, a machine learning model is trained on the extracted druginformation to predict potency, efficacy, primary effects, biologicaltargets, toxicity, secondary effects (off target effects), etc. forother drugs lacking characteristics extracted from the literature. Insome cases, the machine learning module may predict if the cancer drugis associated with a risk of the patient developing a secondary diseaseor cancer over time. At operation 240, drug-based biological targetinformation (obtained from the extracted drug information) is mapped tobiological pathways.

At operation 250, patient specific omic data is obtained, e.g.,indicating a type of cancer of the patient and one or more types ofomics information, which may include genomic sequences (e.g., includingmutations that are associated with cancer, presence of specific drivergenes that are associated with cancer, genes, gene variants, etc.), RNAexpression levels (e.g., including specific transcripts associated withcancer), protein expressions levels (e.g., including one or morebiomarkers associated with cancer, overexpression and/orunderexpression, etc.). In some aspects, omics data may be analyzed andprovided by the omics service provider (e.g., a company performinggenomic sequencing and/or offering microarray analysis or other servicesto evaluate gene translation, protein expression, etc.) A report may beprovided to the patient or medical provider regarding the results of thereport, and may identify a genomic mutation, a gene variant, or specificproteins/transcripts associated with cancer.

At operation 260, patient specific omics data is mapped to biologicalpathways. For example, if the patient specific data shows a mutation ina particular protein or gene of a biological pathway, the system willidentify the protein's or gene's presence in a biological pathway. Oncethe targets are known, the drug ranking system may determine which drugsare most suitable for administration. In some cases, drugs may be mappedto the biological pathway (e.g., to determine which drugs act on abiological pathway containing the patient-specific mutation). Secondaryeffects may also be mapped to the biological pathway. In some cases,secondary effects (off target binding) may be associated with toxicityor other undesirable drug characteristics. Biological pathways may beidentified that are associated with toxicity, and used to identify otherdrugs that may have toxicity issues, based on interaction with theseidentified pathways. At operation 270, a machine learning module istrained on the extracted information, and used to predict drugcharacteristics. At operation 280, a plurality of drugs (or combinationsthereof) are ranked based on a drug effectiveness score to targetspecific cancer pathways relevant to the patient's omics information andcancer type. The drug effectiveness score may reflect weightedcombinations of various drug characteristics (e.g., efficacy, toxicity,potency, etc.).

FIG. 3 shows omics data that may include but is not limited to data fromgenomics, epigenomics, transcriptomics, proteomics, metabolomics, etc.studies. In some aspects, omics data may be obtained from publiclyavailable databases, which may include publications, sequences,expression or transcription levels from microarray analyses, otherresults of omics studies, etc. Omics data may include data from apopulation of patients and may be extracted and stored in extractedomics data 41.

For each of these categories, the data may be analyzed to identifyvarious cancer related targets. For example, genomic/epigenomic data maybe analyzed to identify genes and mutations associated with cancer, aswell as transcription and expression levels of molecules involved in thedevelopment and pathogenesis of cancer. Certain types of cancer may havespecific transcription or expression profiles, which are associated witha biological pathway. This information may be mapped to biologicalpathways to indicate oncogenic mutations and other oncogenic factors.

Thus, omics extractor 72 may identify specific information (e.g.,mutations, transcription profiles, expression profiles, etc.) that areassociated with specific types of cancer for a population ofindividuals. This information may be stored as extracted omics data 41.

When patient specific information (e.g., patient specific data 43) isprovided to the system, biological pathways with cancer relatedinformation from mapped population based omics information may be usedto identify potential drug targets for specific biological targetsand/or pathways associated with the specific patient. Patient specificdata 43 may include genomic information, transcriptomic information,proteomic information, metabolic information, etc. or any other suitablepharmacological or experimental information pertaining to the specificpatient. Based on this approach, specific biological targets and/orpathways may be identified as potential drug targets for the patient.

FIG. 4 shows various biological pathways. In this example, the pathwaysare shown arranged according to categories including cell motility, cellgrowth, cell viability, and cell differentiation and cytostasis. Thenodes represent various entities (e.g., proteins, chemical molecules,etc.) in the pathway which have a particular biological/chemicalstructure. The black arrows show interconnectivity between nodes ofbiological pathways. In this example, the outer circle represents anoutline of the cell, whereas the inner circle represents an outline ofthe nucleus.

A variety of biological pathways may be targeted in a variety of mannersincluding extracellularly, at the cell membrane, inside the cell at thecytoplasmic level, as well as inside the nucleus which controls geneexpression.

Example biological targets are shown as open circles, which correspondto various potential biological targets of drugs. Target 1 (circlecontaining number 1) corresponds to a drug target (e.g., for aparticular drug) with no known secondary interactions. Target 2 (circlecontaining number 2) corresponds to another drug target. Targets 3 and 4represent secondary targets of still other drugs associated withtoxicity.

In the cell growth category, targets 3, 4 are present along the samebiological pathway, both of which reflect secondary targets, and areassociated with drug toxicity. In this case, the drug ranking modulewould consider any target 2 along this same biological pathway to beassociated with toxicity and therefore may not prioritize drugs alongthis pathway over drugs in other non-toxic pathways.

Additionally, drugs that target the same gene, same gene variant, orsame biological pathway may be grouped and the corresponding efficacy,toxicity, side effects, potency, ED50, and IC50 for each drug withrespect to a category may be determined. In some cases, a singleoncogene may be targeted by different drugs having differentcharacteristics. The drug ranking system may rank the drugs in apatient-specific manner, based on the specific drug and/or thepatient-specific omics profile.

FIG. 5 shows an example of analyzing a plurality of drugs anddiscovering new patterns and relationships between drugs and theirtargets. For example, a first drug (drug 1 profile) and a second drug(drug 2 profile) may bind to the same biological target (gene X orprotein Z). The toxicity of the first drug is not known, but may bederived based on extracted information using a machine learning module.

A machine learning module may be used to determine drug characteristics.Training data may be provided to the machine learning module as drugprofiles. Once trained, the machine learning system may predict whetherdrug 1 with an unknown toxicity may have a toxic side effect. In thiscase, the machine learning system may be trained on a plurality of drugprofiles which link toxicity to secondary biological targets. From thisinformation, the machine learning module may determine that drug 1,which binds to secondary biological targets is also likely to have toxicside effects. Accordingly, in this example, drug 2 may be selected forpatient administration to treat a particular type of cancer, as thefirst drug is associated with secondary biological targets linked totoxicity, an undesirable side effect.

As another example, the profiles of a plurality of drugs may beevaluated for toxicity. Drugs having a toxicity above a threshold may begrouped together, and the drugs may be evaluated for common featuresthat are linked to the toxicity. For example, if a group of drugsinteract (secondary effect) with a gene or protein associated withtoxicity, the group of drugs may be evaluated for common structuralfeatures (e.g., presence of a particular side chain, heterocyclic group,etc.) that may be common to all the drugs, and therefore, likelyassociated with the toxicity. In FIG. 5 , drug 3 may be flagged aspotentially having toxicity due to the presence of side chain A1, afeature common to a plurality of other drugs known to have toxicity.Additionally, new drugs having these same features may be flagged fortoxicity. A drug not associated with toxicity may be selected foradministration to the patient.

As yet another example, a group of genes may be evaluated for synergy orlack of synergy. A drug interacting with a first group of genes via offtarget effects (e.g., interacts with A, R, Y genes→demonstrates synergyand does not have toxicity) would be selected over another druginteracting with a second group of genes (e.g., interacts with A, D, Ygenes→demonstrates lack of synergy and has toxicity).

As yet another example, patients that respond to a drug with aparticular side effect may be evaluated to identify causes of the sideeffect. For example, if about three percent of a patient populationexhibits a toxicity effect when taking a specific drug, thepatient-specific data from the adverse population can be compared to acontrol population (those taking the drug without the side effect), todetermine features potentially responsible for the off target effect(e.g., genetic commonalities in the adverse population, commonalities inmedical history, etc.). For example, three percent of patients may havea mutation in a gene, which may lead to an adverse effect, e.g., fromthe drug binding to (increased affinity) the corresponding mutatedprotein.

Accordingly, these techniques allow profiles to be generated forindividual drugs, based on extracted information and machine learning,and the drugs may be scored and ranked using the ranking module 75 todetermine effective drugs for a given patient with a particular type ofcancer.

FIG. 6 shows a flow chart for ranking drugs. Initially, rankings may belimited to strong, intermediate, and low categories. Once the machinelearning module is trained, drugs may be ranked numerically, wherein therank is based on any one or more of a toxicity, a potency, ED50, IC50, aclinical efficacy, etc. for a given biological target. For the ranking,extracted data (e.g., genes, gene variants, biological targets, etc.)may be mapped to biological pathways, which may also be extracted fromthe literature.

At operation 710, patient specific data is obtained, and may be used todetermine a biological target to treat the patient's cancer. Atoperation 720, the system 15 determines which drugs target the gene/genevariant/biological pathway identified from the patient-specific data. Atoperation 730, differentially weighted concepts relating to drugeffectiveness are applied to each drug and a drug effectiveness scoremay be calculated. The drug effectiveness score may represent differentcharacteristics of the drug, depending on what concepts/weights areapplied, including but not limited to efficacy, toxicity, potency,patient-specific factors (e.g., omic information, medical history), etc.Efficacy is the maximum effect of a drug (regardless of dose). Potencyis the amount of a drug that is needed to produce a specified effect.Toxicity corresponds to an amount of a drug leading to an adverse effect(e.g., difficulty breathing, organ damage etc.).

In some cases, drugs that target the same gene, gene variant and/orsignaling pathway may be grouped together, and each ranked usingdifferentially weighted concepts related to drug effectiveness that areapplied to each drug. A drug effectiveness score is calculated, whereinthe drug effectiveness score may represent different characteristics ofthe drug, depending on what concepts/weights are applied.

In other cases, each drug may be ranked based on a drug effectivenessscore in terms of effectiveness (e.g., using differentially weightedconcepts related to drug characteristics including toxicity, efficacy,secondary effects, potency, etc.) at the gene, gene variant and/orsignaling pathway level (not limited by the same target).

In some aspects, drug ranking may assign drugs to tiers, with tier 1having highest/best efficacy based on the tumor response or IC50 value;tier 2 having moderate/mid efficacy based on the tumor response or IC50value; and tier 3 having low/no efficacy based on the tumor response orIC50 value. In some cases, drugs that target the same gene, same genevariant, same biological pathway may be grouped by tier level.

Thus, drugs may be ranked according to tiers (without being limited tothe same gene, variant, or pathway). Once tier ranking is complete, eachtier may undergo further ranking, by grouping drugs that target the samegene/gene variant or biological pathway.

Additional types of information may be provided about specific drugs,such as regulatory approval status (in the FDA and in non-US countries),known associations with drug resistance, whether the drug passes theblood-brain barrier, and the chemical structure of the drug, which maybe considered in the analysis and ranking as well.

This information may be integrated into a data management portal forcase management. Drugs that target a specific gene, gene mutation, orbiological pathway may be presented to a physician or other health careprovider with drug rankings to drive selection of treatment options fora patient with a particular gene alteration, gene mutation or biologicalpathway alteration.

FIG. 7 shows a flow chart of example operations. At operation 810,extracted information pertaining to drug characteristics for a pluralityof drugs is analyzed. At operation 815, for each drug, one or more drugcharacteristics are determined. Drug characteristics may include atoxicity, a potency and an efficacy. At operation 820, each drug isassociated with a plurality of attributes, including a variant or a genetargeted by the drug, and a biological pathway comprising the variant orthe gene. In some cases, gene names may include gene name synonyms andgene variant name synonyms, and drug names may include drug namesynonyms. At operation 825, the plurality of drugs are ranked using adrug effectiveness score based upon one or more drug characteristicsselected from the group consisting of efficacy, potency, and/ortoxicity.

Drugs may be combined based on one or more of the following criteriaincluding specific diseases, genes, gene synonyms, gene variants, genevariant synonyms drugs, drug name synonyms, cancer-types and cancer-typename synonyms.

Present techniques provide a variety of advantages over existingapproaches including generating a multi-tiered system to rank drugsimpacting the same target. The first tier may include extracting genes,gene variants, and/or signaling pathways from literature and/ordatabases. The second tier ranks each drug in terms of efficacy or otherdrug characteristics (e.g., toxicity, secondary effects, potency, etc.)at the gene, gene variant and/or signaling pathway level. The third tiergroup ranks drugs together that target the same gene, gene variantand/or signaling pathway. Present techniques may use also preclinicaldata (extracted IC50 values) and/or clinical trials (potency) from theliterature for ranking drugs. Cancer-specific information may beextracted, and utilized in conjunction with machine learning forpersonalized genomics-based medicine.

It will be appreciated that the embodiments described above andillustrated in the drawings represent only a few of the many ways ofimplementing embodiments for ranking drugs according to administration(e.g., efficacy, toxicity, patient specificity, potency, off targeteffects, etc.).

The environment of the present invention embodiments may include anynumber of computer or other processing systems (e.g., client or end-usersystems, server systems, etc.) and databases or other repositoriesarranged in any desired fashion, where the present invention embodimentsmay be applied to any desired type of computing environment (e.g., cloudcomputing, client-server, network computing, mainframe, stand-alonesystems, etc.). The computer or other processing systems employed by thepresent invention embodiments may be implemented by any number of anypersonal or other type of computer or processing system (e.g., desktop,laptop, PDA, mobile devices, etc.), and may include any commerciallyavailable operating system and any combination of commercially availableand custom software (e.g., browser software, communications software,server software, drug ranking system, etc.). These systems may includeany types of monitors and input devices (e.g., keyboard, mouse, voicerecognition, etc.) to enter and/or view information.

It is to be understood that the software (e.g., drug terms extractor 71,omics extractor 72, machine learning module 73, biological pathwaysmodule 74, drug ranking module 75, etc.) of the present inventionembodiments may be implemented in any desired computer language andcould be developed by one of ordinary skill in the computer arts basedon the functional descriptions contained in the specification and flowcharts illustrated in the drawings. Further, any references herein ofsoftware performing various functions generally refer to computersystems or processors performing those functions under software control.The computer systems of the present invention embodiments mayalternatively be implemented by any type of hardware and/or otherprocessing circuitry.

The various functions of the computer or other processing systems may bedistributed in any manner among any number of software and/or hardwaremodules or units, processing or computer systems and/or circuitry, wherethe computer or processing systems may be disposed locally or remotelyof each other and communicate via any suitable communications medium(e.g., LAN, WAN, Intranet, Internet, hardwire, modem connection,wireless, etc.). For example, the functions of the present inventionembodiments may be distributed in any manner among the variousend-user/client and server systems, and/or any other intermediaryprocessing devices. The software and/or algorithms described above andillustrated in the flow charts may be modified in any manner thataccomplishes the functions described herein. In addition, the functionsin the flow charts or description may be performed in any order thataccomplishes a desired operation.

The software of the present invention embodiments (e.g., drug termsextractor 71, omics extractor 72, machine learning module 73, biologicalpathways module 74, drug ranking module 75, etc.) may be available on anon-transitory computer useable medium (e.g., magnetic or opticalmediums, magneto-optic mediums, floppy diskettes, CD-ROM, DVD, memorydevices, etc.) of a stationary or portable program product apparatus ordevice for use with stand-alone systems or systems connected by anetwork or other communications medium.

The communication network may be implemented by any number of any typeof communications network (e.g., LAN, WAN, Internet, Intranet, VPN,etc.). The computer or other processing systems of the present inventionembodiments may include any conventional or other communications devicesto communicate over the network via any conventional or other protocols.The computer or other processing systems may utilize any type ofconnection (e.g., wired, wireless, etc.) for access to the network.Local communication media may be implemented by any suitablecommunication media (e.g., local area network (LAN), hardwire, wirelesslink, Intranet, etc.).

The system may employ any number of any conventional or other databases,data stores or storage structures (e.g., files, databases, datastructures, data or other repositories, etc.) to store information(e.g., extracted omics data 41, extracted literature data 42, patientspecific data 43, drug ranking data 44, etc.). The database system maybe implemented by any number of any conventional or other databases,data stores or storage structures (e.g., files, databases, datastructures, data or other repositories, etc.) to store information(e.g., extracted omics data 41, extracted literature data 42, patientspecific data 43, drug ranking data 44, etc.). The database system maybe included within or coupled to the server and/or client systems. Thedatabase systems and/or storage structures may be remote from or localto the computer or other processing systems, and may store any desireddata (e.g., extracted omics data 41, extracted literature data 42,patient specific data 43, drug ranking data 44, etc.).

The present invention embodiments may employ any number of any type ofuser interface (e.g., Graphical User Interface (GUI), command-line,prompt, etc.) for obtaining or providing information (e.g., extractedomics data 41, extracted literature data 42, patient specific data 43,drug ranking data 44, etc.), where the interface may include anyinformation arranged in any fashion. The interface may include anynumber of any types of input or actuation mechanisms (e.g., buttons,icons, fields, boxes, links, etc.) disposed at any locations toenter/display information and initiate desired actions via any suitableinput devices (e.g., mouse, keyboard, etc.). The interface screens mayinclude any suitable actuators (e.g., links, tabs, etc.) to navigatebetween the screens in any fashion.

The report may include a listing of ranked drugs along with any otherinformation arranged in any fashion, and may be configurable based onrules or other criteria to provide desired information to a user (e.g.,efficacies, toxicity, patient-specific, etc.).

The present invention embodiments are not limited to the specific tasksor algorithms described above, but may be utilized for any applicationin which a ranking of drugs is needed based upon characteristicsdispersed throughout a corpus comprising unstructured and/or structureddocuments.

The terminology used herein is for the purpose of describing particularembodiments only and is not intended to be limiting of the invention. Asused herein, the singular forms “a”, “an” and “the” are intended toinclude the plural forms as well, unless the context clearly indicatesotherwise. It will be further understood that the terms “comprises”,“comprising”, “includes”, “including”, “has”, “have”, “having”, “with”and the like, when used in this specification, specify the presence ofstated features, integers, steps, operations, elements, and/orcomponents, but do not preclude the presence or addition of one or moreother features, integers, steps, operations, elements, components,and/or groups thereof.

The corresponding structures, materials, acts, and equivalents of allmeans or step plus function elements in the claims below are intended toinclude any structure, material, or act for performing the function incombination with other claimed elements as specifically claimed. Thedescription of the present invention has been presented for purposes ofillustration and description, but is not intended to be exhaustive orlimited to the invention in the form disclosed. Many modifications andvariations will be apparent to those of ordinary skill in the artwithout departing from the scope and spirit of the invention. Theembodiment was chosen and described in order to best explain theprinciples of the invention and the practical application, and to enableothers of ordinary skill in the art to understand the invention forvarious embodiments with various modifications as are suited to theparticular use contemplated.

The descriptions of the various embodiments of the present inventionhave been presented for purposes of illustration, but are not intendedto be exhaustive or limited to the embodiments disclosed. Manymodifications and variations will be apparent to those of ordinary skillin the art without departing from the scope and spirit of the describedembodiments. The terminology used herein was chosen to best explain theprinciples of the embodiments, the practical application or technicalimprovement over technologies found in the marketplace, or to enableothers of ordinary skill in the art to understand the embodimentsdisclosed herein.

The present invention may be a system, a method, and/or a computerprogram product at any possible technical detail level of integration.The computer program product may include a computer readable storagemedium (or media) having computer readable program instructions thereonfor causing a processor to carry out aspects of the present invention.

The computer readable storage medium can be a tangible device that canretain and store instructions for use by an instruction executiondevice. The computer readable storage medium may be, for example, but isnot limited to, an electronic storage device, a magnetic storage device,an optical storage device, an electromagnetic storage device, asemiconductor storage device, or any suitable combination of theforegoing. A non-exhaustive list of more specific examples of thecomputer readable storage medium includes the following: a portablecomputer diskette, a hard disk, a random access memory (RAM), aread-only memory (ROM), an erasable programmable read-only memory (EPROMor Flash memory), a static random access memory (SRAM), a portablecompact disc read-only memory (CD-ROM), a digital versatile disk (DVD),a memory stick, a floppy disk, a mechanically encoded device such aspunch-cards or raised structures in a groove having instructionsrecorded thereon, and any suitable combination of the foregoing. Acomputer readable storage medium, as used herein, is not to be construedas being transitory signals per se, such as radio waves or other freelypropagating electromagnetic waves, electromagnetic waves propagatingthrough a waveguide or other transmission media (e.g., light pulsespassing through a fiber-optic cable), or electrical signals transmittedthrough a wire.

Computer readable program instructions described herein can bedownloaded to respective computing/processing devices from a computerreadable storage medium or to an external computer or external storagedevice via a network, for example, the Internet, a local area network, awide area network and/or a wireless network. The network may comprisecopper transmission cables, optical transmission fibers, wirelesstransmission, routers, firewalls, switches, gateway computers and/oredge servers. A network adapter card or network interface in eachcomputing/processing device receives computer readable programinstructions from the network and forwards the computer readable programinstructions for storage in a computer readable storage medium withinthe respective computing/processing device.

Computer readable program instructions for carrying out operations ofthe present invention may be assembler instructions,instruction-set-architecture (ISA) instructions, machine instructions,machine dependent instructions, microcode, firmware instructions,state-setting data, configuration data for integrated circuitry, oreither source code or object code written in any combination of one ormore programming languages, including an object oriented programminglanguage such as Smalltalk, C++, or the like, and procedural programminglanguages, such as the “C” programming language or similar programminglanguages. The computer readable program instructions may executeentirely on the user's computer, partly on the user's computer, as astand-alone software package, partly on the user's computer and partlyon a remote computer or entirely on the remote computer or server. Inthe latter scenario, the remote computer may be connected to the user'scomputer through any type of network, including a local area network(LAN) or a wide area network (WAN), or the connection may be made to anexternal computer (for example, through the Internet using an InternetService Provider). In some embodiments, electronic circuitry including,for example, programmable logic circuitry, field-programmable gatearrays (FPGA), or programmable logic arrays (PLA) may execute thecomputer readable program instructions by utilizing state information ofthe computer readable program instructions to personalize the electroniccircuitry, in order to perform aspects of the present invention.

Aspects of the present invention are described herein with reference toflowchart illustrations and/or block diagrams of methods, apparatus(systems), and computer program products according to embodiments of theinvention. It will be understood that each block of the flowchartillustrations and/or block diagrams, and combinations of blocks in theflowchart illustrations and/or block diagrams, can be implemented bycomputer readable program instructions.

These computer readable program instructions may be provided to aprocessor of a general purpose computer, special purpose computer, orother programmable data processing apparatus to produce a machine, suchthat the instructions, which execute via the processor of the computeror other programmable data processing apparatus, create means forimplementing the functions/acts specified in the flowchart and/or blockdiagram block or blocks. These computer readable program instructionsmay also be stored in a computer readable storage medium that can directa computer, a programmable data processing apparatus, and/or otherdevices to function in a particular manner, such that the computerreadable storage medium having instructions stored therein comprises adocument of manufacture including instructions which implement aspectsof the function/act specified in the flowchart and/or block diagramblock or blocks.

The computer readable program instructions may also be loaded onto acomputer, other programmable data processing apparatus, or other deviceto cause a series of operational steps to be performed on the computer,other programmable apparatus or other device to produce a computerimplemented process, such that the instructions which execute on thecomputer, other programmable apparatus, or other device implement thefunctions/acts specified in the flowchart and/or block diagram block orblocks.

The flowchart and block diagrams in the Figures illustrate thearchitecture, functionality, and operation of possible implementationsof systems, methods, and computer program products according to variousembodiments of the present invention. In this regard, each block in theflowchart or block diagrams may represent a module, segment, or portionof instructions, which comprises one or more executable instructions forimplementing the specified logical function(s). In some alternativeimplementations, the functions noted in the blocks may occur out of theorder noted in the Figures. For example, two blocks shown in successionmay, in fact, be executed substantially concurrently, or the blocks maysometimes be executed in the reverse order, depending upon thefunctionality involved. It will also be noted that each block of theblock diagrams and/or flowchart illustration, and combinations of blocksin the block diagrams and/or flowchart illustration, can be implementedby special purpose hardware-based systems that perform the specifiedfunctions or acts or carry out combinations of special purpose hardwareand computer instructions.

What is claimed is:
 1. A method of determining drug characteristicscomprising: analyzing extracted information pertaining to drugadministration for a plurality of drugs, wherein the extractedinformation is extracted using a natural language processing model;determining for each drug, one or more drug characteristics for aplurality of attributes corresponding to the drug, wherein theattributes include a variant or a gene targeted by the drug, and abiological pathway comprising the variant or the gene; identifying, forthe plurality of drugs, common structural features and correspondingdrug characteristics; identifying which of the drugs are associated withtoxicity; predicting, using a machine learning module trained oninformation identifying the common structural features and the drugcharacteristics, other drugs of the plurality of drugs associated withtoxicity; and ranking the plurality of drugs according to a drugeffectiveness score based on the determined drug characteristics for theplurality of attributes that include the variant or gene targeted by thedrug, and the biological pathway comprising the variant or gene, whereindrugs that are more highly associated with toxicity are ranked as lesseffective according to the drug effectiveness score.
 2. The method ofclaim 1, wherein the drug characteristics are selected from one or moreof the group consisting of efficacy, the toxicity, and potency.
 3. Themethod of claim 1, wherein the extracted information comprisespre-clinical, clinical, and post clinical information.
 4. The method ofclaim 3, comprising: training a predictive machine learning module withthe extracted information; predicting one or more drug characteristicsfor each drug of the plurality of drugs based on the trained predictivemachine learning module; and ranking the drugs for treatment of apatient-specific cancer, wherein each drug targets a particular gene,gene variant, or biological pathway associated with the patient'scancer, and based on the predicted drug characteristics.
 5. The methodof claim 1, wherein the plurality of drugs are classified into groupsbased on a common target, and ranking the drugs within each group. 6.The method of claim 1, wherein the attributes comprise patient-specificinformation indicating the gene, the variant or the biological pathwayfor a patient, and further comprising: identifying the plurality ofdrugs that target the gene, gene variant or biological pathway of thepatient; and ranking the identified drugs for the patient based on thedrug effectiveness score.
 7. The method of claim 1, wherein theextracted information further includes patient-specific omic data.
 8. Acomputer system for determining drug characteristics wherein the systemcomprises at least one processor configured to: analyze extractedinformation pertaining to drug administration for a plurality of drugs,wherein the extracted information is extracted using a natural languageprocessing model; determine for each drug, one or more drugcharacteristics for a plurality of attributes corresponding to the drug,wherein the attributes include a variant or a gene targeted by the drug,and a biological pathway comprising the variant or the gene; identify,for the plurality of drugs, common structural features and correspondingdrug characteristics; identify which of the drugs are associated withtoxicity; predict, using a machine learning module trained oninformation identifying the common structural features and the drugcharacteristics, other drugs of the plurality of drugs associated withtoxicity; and rank the plurality of drugs according to a drugeffectiveness score based on the determined drug characteristics for theplurality of attributes that include the variant or gene targeted by thedrug, and the biological pathway comprising the variant or gene, whereindrugs that are more highly associated with toxicity are ranked as lesseffective according to the drug effectiveness score.
 9. The system ofclaim 8, wherein the drug characteristics are selected from one or moreof the group consisting of efficacy, the toxicity, and potency.
 10. Thesystem of claim 8, wherein the extracted information comprisespre-clinical, clinical, and post clinical information.
 11. The system ofclaim 10, wherein the processor is further configured to: train apredictive machine learning module with the extracted information;predict one or more drug characteristics for each drug of the pluralityof drugs based on the trained predictive machine learning module; andrank the drugs for treatment of a patient-specific cancer, wherein eachdrug targets a particular gene, gene variant, or biological pathwayassociated with the patient's cancer, and based on the predicted drugcharacteristics.
 12. The system of claim 8, wherein the plurality ofdrugs are classified into groups based on a common target, and whereinthe processor is further configured to rank the drugs within each group.13. The system of claim 8, wherein the attributes comprisepatient-specific information indicating the gene, the variant or thebiological pathway for a patient, and wherein the processor is furtherconfigured to: identify the plurality of drugs that target the gene,gene variant or biological pathway of the patient; and rank theidentified drugs for the patient based on the drug effectiveness score.14. The computer system of claim 8, wherein the extracted informationfurther includes patient-specific omic data.
 15. A computer programproduct for determining drug characteristics, the computer programproduct comprising one or more computer readable storage mediacollectively having program instructions embodied therewith, the programinstructions executable by a computer to cause the computer to: analyzeextracted information pertaining to drug administration for a pluralityof drugs, wherein the extracted information is extracted using a naturallanguage processing model; determine for each drug, one or more drugcharacteristics for a plurality of attributes corresponding to the drug,wherein the attributes include a variant or a gene targeted by the drug,and a biological pathway comprising the variant or the gene; identify,for the plurality of drugs, common structural features and correspondingdrug characteristics; identify which of the drugs are associated withtoxicity; predict, using a machine learning module trained oninformation identifying the common structural features and the drugcharacteristics, other drugs of the plurality of drugs associated withtoxicity; and rank the plurality of drugs according to a drugeffectiveness score based on the determined drug characteristics for theplurality of attributes that include the variant or gene targeted by thedrug, and the biological pathway comprising the variant or gene, whereindrugs that are more highly associated with toxicity are ranked as lesseffective according to the drug effectiveness score.
 16. The computerprogram product of claim 15, wherein the drug characteristics areselected from one or more of the group consisting of efficacy, thetoxicity, and potency.
 17. The computer program product of claim 15,wherein the extracted information comprises pre-clinical, clinical, andpost clinical information.
 18. The computer program product of claim 15,wherein the program instructions are further executable to: train apredictive machine learning module with the extracted information;predict one or more drug characteristics for each drug of the pluralityof drugs based on the trained predictive machine learning module; andrank the drugs for treatment of a patient-specific cancer, wherein eachdrug targets a particular gene, gene variant, or biological pathwayassociated with the patient's cancer, and based on the predicted drugcharacteristics.
 19. The computer program product of claim 15, whereinthe attributes comprise patient-specific information indicating thegene, the variant or the biological pathway for a patient, and whereinthe program instructions are further executable to: identify theplurality of drugs that target the gene, gene variant or biologicalpathway of the patient; and rank the identified drugs for the patientbased on the drug effectiveness score.
 20. The computer program productof claim 15, wherein the extracted information further includespatient-specific omic data.